Bridging the Deployment Gap: Integrating AI into Accelerator Control Systems
TL;DR
Cosylab proposes a framework-agnostic abstraction layer that connects AI services to facility control systems without necessarily modifying operational infrastructure, and explores the possibility of using Large Language Model applications as operator copilots and actuating agents, each with distinct safety requirements.
Abstract
A persistent gap separates research-grade Artificial Intelligence (AI) tools from operational accelerator control systems—one that is defined by integration engineering, not model performance. Differences between major control system frameworks—Experimental Physics and Industrial Control System (EPICS), TANGO Controls (TANGO), OPC Unified Architecture (OPC-UA), Distributed Object-Oriented Control System (DOOCS), and Front-End Software Architecture (FESA)—combined with network security constraints, data quality requirements, and operator trust, creates challenges absent from research environments. Cosylab, as an industrial partner within the ARTificial Intelligence For Accelerators, user Communities and associated Technologies (ARTIFACT) network, investigate the possibilities to address this. We propose a framework-agnostic abstraction layer that connects AI services to facility control systems without necessarily modifying operational infrastructure. The architecture enforces safety by default: read-only access, scope-restricted writes, Human-in-the-Loop (HITL) approval, watchdog mechanisms, and audit logging. We also explore the possibility of using Large Language Model (LLM) applications as operator copilots—alarm interpretation, procedure assistance, knowledge retrieval—and actuating agents, each with distinct safety requirements. This integration layer forms the basis of Cosylab’s contribution to TwinRise, an EU-funded Digital Twin Engine project.